Predictors of No Show and Patient Cancellation at An Outpatient Autism Clinic

Background: Patient appointment absenteeism has a deleterious effect on the global functioning of healthcare facilities and ultimately, public health (Melnikow & Kiefe, 1994; Sharp & Hamilton, 2001). No Shows (NS) and Patient Cancellations (PC) can lead to longer waiting times, clinic fiscal instability, poor utilization of personnel, and an overall decline in the timeliness and quality of care (Satiani, 2009). This decrease in quality service delivery is alarming for specialized autism settings given the growing prevalence of Autism Spectrum Disorders (ASD) in the US, the scarcity of autism service providers and clinics, and the preexisting long wait times within these centers. Despite this hazard, there is a dearth of research that has examined the rates or predictors of NS and PC in community autism clinics.

Objectives: The aim of this study was to examine attendance rates at an outpatient autism center and explore what factors influence whether patients NS or PC for appointments.

Methods: Longitudinal data from eight hundred and twelve children ages five months to seventeen years (M=7y) were used for this study. Appointment data from 2003 to 2009 were gathered from the local scheduling information system and additional demographic information was collected from a research registration form.  A random effects logistic regression model was employed to examine predictors of patient absenteeism. This approach is most useful when making inferences about individuals rather than population averages, given the heterogeneity of our sample (overall r²=.01).  Time-dependent predictors included provider type, patient age, academic semester, wait time, and appointment time. Static demographic predictors included race/ethnicity, gender, distance from the clinic, and history of medical assistance, serving as a proxy for low family income. Separate models were developed to independently examine our dependent variables: NS and PC.

Results: A total of 14,714 appointments were analyzed (M=108, p50=40, Max=1062), of which 1,470 (12%) were NS and 991 (8%) were PC.  Receiving medical assistance (OR 1.78), non-white families (OR 1.49), wait-time beyond 60 days (OR 1.25), winter school semester (OR 1.26), and having a non-evaluation appointment type (OR 2.36) increased the risk of NS. Having an appointment with an MD (OR .54), female children (OR .71), and a wait time less than 60 days (OR .73) decreased the likelihood of NS (all p<.05).  Having a child older than 8 years of age (OR 1.32), living further than 10 miles from the clinic (OR 1.34), non-white families (OR1 .47), a wait time beyond 60 days (OR 1.59), an evening appointment time (OR 1.26), and an appointment with an OT (OR 1.46) increased the risk of PC. An appointment with an MD (OR .53) or psychologist (OR .57), and an appointment during the spring semester (OR .80) was protective (all p<.05).

Conclusions: To our knowledge, this is the first study to investigate rates and predictors of patient absenteeism at an outpatient autism center. This data identifies numerous child, family, and clinic factors associated with both NS and PC. These data hold important implications to the prospective design of interventions seeking to reduce this burden.